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Citronellol Reduces Sepsis-Induced Renal Inflammation via AP-1/NF-κB/TNF-α Pathway
No full textSepsis is characterized by the over-production of pro-inflammatory cytokines. Cecal ligation and puncture (CLP) is a well-accepted model for recreating sepsis-induced renal injury in mice. The current study investigates how citronellol, a naturally occurring substance with a variety of biological characteristics, can prevent acute kidney inflammation brought on by CLP. In the CLP mouse model, citronellol was administered orally at doses of 50 and 100 mg/kg. Serum levels of creatinine and urea were used as markers of renal function, and the Murine Sepsis Score (MSS) was used to assess the severity of sepsis. According to our findings, CLP caused a decline in renal function, as shown by higher serum urea and creatinine levels in comparison to control mice. Nevertheless, administering citronellol as pretreatment at doses of 50 and 100 mg/kg alleviated the deterioration in renal functions. Citronellol decreased levels of serum urea and creatinine. Citronellol demonstrated an anti-inflammatory effect by reducing pro-inflammatory cytokines (TNF-α, NF-κB, AP-1) and KIM-1. Overall, our study suggests that citronellol holds a promise as a potential therapeutic agent for mitigating kidney inflammation
From Digital Divide to Equity-Enhancing Diffusion: Generative AI and Writing Quality
No full textThis study investigates whether generative AI can narrow the gap between stronger and developing writers and explores the mechanisms underlying these effects. In a within-subject experiment, students wrote two essays, with and without AI assistance. Computer-aided analysis of the writing quality confirmed that while all students benefited from AI, that less skillful writers gained more. There was also no evidence of skillful writers using AI in more sophisticated and beneficial ways. The study contributes to theorizing the digital divide and offers insights into maximizing the benefits of AI tools. Theoretically, we situate generative-AI use within Diffusion of Innovations, treating ChatGPT as an innovation whose consequences depend on implementation quality (prompting and editing), and we show equity-enhancing—rather than inequality-widening—consequences under specific use patterns
Establishing Convergence Thresholds for Pre-Trajectory Sampling with Batched Execution Across Random Quantum Circuits
No full textA crucial aspect of validating quantum protocols is understanding the noise produced by quantum computing devices. Using simulations that can replicate this noise allows for a lower-cost alternative to hardware experiments. Stochastic, so-called trajectory methods are often used as a quadratically reduced approximation to density matrix simulations, but traditional implementations have limited sampling capacity and provide no error-based metadata. The Pre-Trajectory Sampling with Batched Execution (PTSBE) [Patti et al., 2025] algorithm provides a solution by combining fine-tuned, well-documented noise sampling with computational intermediate caching.
While the original work is effective on quantum error correction circuits, its performance on general circuits has yet to be explored. This project gauges PTSBE on random quantum circuits to understand convergence behavior and accuracy compared to traditional methods. To accomplish this, an automated simulation pipeline was developed by using Apache Airflow and CUDA-Q to generate a collection of noisy circuits, run both exact and approximate simulations, and analyze error criteria across various qubit counts and circuit depths.
Early tests have confirmed the system\u27s ability to scale to 20-qubit circuits and produce accurate comparison data. Our work aims to provide guidance for researchers using PTSBE in noisy simulation environments, expanding its use beyond specific cases
Physics-Informed Deep Learning Reveals Climate-Driven Snowpack Decline and Threatens Ecological Water Availability in a Californian Snow-Fed Catchment
No full textMountain snowpack functions as a critical natural reservoir, and its gradual melt sustains streamflow for downstream ecosystems, particularly in arid and semi-arid regions. Climate warming is disrupting these historical patterns by shifting precipitation from snow to rain and accelerating melt, creating an urgent need for robust ecological forecasting tools that can anticipate threats to water availability. Physics-informed machine learning (PIML) offers a powerful approach to this challenge by integrating domain knowledge into flexible data-driven models. This study evaluates and compares the performance of three hydrological modeling approaches: (i) a calibrated process-based Soil and Water Assessment Tool (SWAT), (ii) a data-driven Long Short-Term Memory (LSTM) neural network, and (iii) a physics-informed LSTM (PIML) model that integrates a melt index and precipitation-phase constraints within the Upper West Walker River Watershed in California, USA. The models are assessed based on their ability to simulate historical daily snow water equivalent (SWE) and streamflow using performance metrics such as Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), root mean square error (RMSE), and peak-timing bias. Results show that the PIML model provides the most robust and balanced performance, particularly in minimizing bias, highlighting the benefit of embedding physically meaningful constraints into data-driven models in snow-dominated basins. Future simulations using bias-corrected CMIP6 high-emission scenarios project that peak SWE may decline by up to 60 % (52–73 %), and peak discharge by approximately 33 % (28–47 %). Moreover, warming-induced changes in precipitation phase are expected to shift snowmelt and runoff 10–19 days earlier, compressing the hydrologic season and inducing ecological stress. These findings emphasize the dual risk of increased springtime flows and diminished summer water availability and heightened ecological drought risk, highlighting the urgent need for adaptive water management strategies in the face of climate-driven hydrological shifts
How Organizational Characteristics Influence the Choice of Performance Measures: A Large-Scale Empirical Study
No full textDespite the growing emphasis on performance measurement, empirical evidence linking the selection of performance measures to distinct organizational characteristics remains limited. Moreover, practical guidance on selecting appropriate performance measures for different organizational contexts is often ambiguous or absent. This study addresses these gaps through a large-scale empirical analysis of 372 organizations across diverse industry sectors. It provides validation for the hypothesized contingency effects of various organizational characteristics—such as size, structure, and global exposure—on performance measurement choices. The findings support our contention that no single performance measure is universally optimal; rather, the suitability of specific measures depends on the organization’s contextual attributes. By integrating both operational and relational performance dimensions, the study offers a comprehensive framework for understanding how organizational contingencies shape measurement practices. The results yield actionable implications for managers, enabling them to align their performance measurement systems with organizational characteristics, thereby enhancing resource allocation, strategic decision-making, and their ability to compete
Dynamic, Reconfigurable, and Hierarchical Biosynthetic Composites via Collagen Self-Assembly within Highly Crowded Microgel Pastes
No full textThe fabrication of a new class of biomimetic biomaterials is reported using nanostructured microgel pastes formed from “overpacked” assemblies of ultrasoft poly(N-isopropyl acrylamide-co-acrylic acid) microgels and their composites with collagen. Despite the solid-like nature of microgel pastes, collagen fibrillogenesis is robust and rapid, with a 3D collagen network forming throughout the paste volume. Structural organization within the composite is interrogated via a suite of microscopy methods, while rheological characterization provides insight into the static and dynamic mechanical properties of the materials. Long-range fibrillogenesis is enabled by local crowding, dynamics, and spatial reconfigurability of pastes at the colloidal length-scale, and by liquid–liquid phase separation during fibril formation, features that mimic the dynamic reorganization of natural extracellular matrix. In vitro 3D cell culture studies illustrate that the paste is non-toxic, permeable to nutrients, and permissive to cell invasion, while collagen fibers present sites for cell attachment and spreading. Together, these results suggest the platform\u27s potential in the development of tissue scaffolds that mimic crowded and dynamic biological tissues. These materials address the need for new approaches to biomaterials that offer dynamic, bio-integrative environments for tissue healing and regenerative medicine via synthetic and spatial control from the polymer to the macroscopic length scales
Evaluating the Effect of Ethnicity and Amyloid on Gait Speed in the Healthy Aging Brain Study-Health Disparities Study
No full textBackground
Many novel Alzheimer\u27s disease (AD) biomarkers have been studied in non-Hispanic White populations, despite Hispanic/Latino adults being 1.5 times more likely to develop AD and receiving a later diagnosis. Equitable, accessible assessments are needed to identify individuals at risk earlier. While cognitive screening monitors early AD signs, neuropsychological tests are often inequitable across ethnicity, race, and other social determinants of health. However, we have shown that performance-based motor assessments may be less susceptible to demographic factors than memory tests, and may be more equitable for screening older adults. Gait speed has been linked to elevated amyloid in non-Hispanic White adults, but this relationship in Hispanic/Latino adults is unknown. This study compared the relationship between gait speed and amyloid in cognitively unimpaired non-Hispanic White and Hispanic/Latino older adults. Method
This study included 597 adults (67.39% female, mean age: 63.47±8.51, 46.66% Hispanic) with a Clinical Dementia Rating of 0 from Wave 1 of the Healthy Aging Brain Study-Health Disparities. Multiple linear regression was used to analyze the effects of ethnicity, sex, age, diabetes, hypertension, and amyloid on gait time (time to walk 4 meters, where lower is better), with interactions between ethnicity and amyloid, and ethnicity and diabetes. The interaction between age and gender was also explored. Amyloid positivity was determined using the florbetaben global standardized uptake value ratio (SUVR) threshold (\u3e1.08). Gait time was evaluated as a continuous variable (seconds), while sex (M/F), diabetes (Y/N), hypertension (Y/N), and amyloid status (positive/negative) were categorical variables. Result
Non-Hispanic White adults were older than Hispanic/Latino adults (mean age: 66.94±.7.92 vs 59.49±.7.37, p \u3c .05), yet had significantly lower gait times (mean gait times: 3.87±0.83 sec vs 4.11±0.85 sec, p = .0028). Differences in gait time due to ethnicity was not explained by diabetes (p = .32), hypertension (p = .87), amyloid (p = .80), or an ethnicity by amyloid interaction (p = .87). Although the model was significant (p \u3c .0001), little variance was explained (adj R2 = .07). Conclusion
Gait speed may reflect risk factors associated with AD and related dementias, but it was not associated with AD-specific pathology. Future research will explore gait time\u27s susceptibility to social determinants of health
The Kaczmarz Algorithm in Hilbert C*-modules
No full textThe Kaczmarz algorithm in Hilbert spaces is a classical iterative method for stably recovering vectors from inner product data. In this paper, we extend the algorithm to the setting of Hilbert C*-modules and establish analogues of its effectiveness in both finite-dimensional and stationary cases. Consequently, we demonstrate that continuous families of elements in a Hilbert space can be uniformly recovered using the Kaczmarz algorithm. Additionally, we develop a normalized Cauchy transform for continuous families of measures and use it to provide sufficient conditions under which standard frames in Hilbert C(X)-modules can be generated by the Kaczmarz algorithm and realized as orbits of bounded operators
The Differential Effects of Fast Walking Speed on Muscle Coactivation in the Paretic and Non-Paretic Extremities Post-Stroke
No full textBackground:
Clinical practice guidelines for walking recovery post-stroke recommend high aerobic intensity training, which usually involves walking at fast speeds. However, the acute effect of fast speeds on the neuromuscular control of walking is unclear. Objectives:
(1) Assess the criterion validity of the Dynamic Motor Control Index (WalkDMC) as a measure of coactivation post-stroke. (2) Assess acute speed-dependent coactivation post-stroke. (3) Assess how clinical characteristics shape the speed-dependent coactivation response. (4) Assess the relationship between heart rate and coactivation post-stroke. We hypothesized that WalkDMC is correlated with function and impairment measures. We also hypothesize that coactivation measured via the WalkDMC increases for speeds above or below self-selected speeds (SSS). Methods:
32 chronic stroke survivors and 17 age and sex-matched controls walked at SSS, fast, and slow speeds. EMGs were measured bilaterally on 7 lower extremity muscles. We used non-negative matrix factorization to calculate WalkDMC. We used regression to assess the relationship between WalkDMC, speed, heart rate, and clinical outcomes. Results:
WalkDMC was correlated with clinical outcomes, supporting its criterion validity. We observed a quadratic relationship between speed and coactivation: for the paretic extremity, the predicted speed that would lead to the lowest coactivation was ~120% higher than SSS. Slow speeds consistently increased coactivation in controls and participants post-stroke. Coactivation in the paretic extremity was significantly predicted by speed, balance, and impairment. Conclusions:
Our results suggest that increased speeds lead to differential improvements in coactivation in the paretic and non-paretic extremities. These results may inform speed prescriptions for HIT interventions
Climate Change Has Increased Global Evaporative Demand Except in South Asia
No full textClimate change alters how strongly the atmosphere draws water from the land, yet a consistent global assessment of this evaporative demand has been lacking. Here, we analyze 45 years of climate data and global models to quantify trends in the key drivers—air temperature, humidity, radiation, wind speed, and cloud cover—that determine the atmosphere’s drying power. We find that evaporative demand has increased worldwide, indicating a stronger atmospheric thirst, except in South Asia, where it has declined. There, widespread irrigation has increased soil and air moisture, enhanced cloud formation, and reduced sunlight reaching the surface, counteracting the global signal. These contrasting trends reveal how human water use can locally reshape the climate’s influence on the water cycle